Systems and methods for detecting worsening heart failure

ABSTRACT

Systems and methods for detecting worsening cardiac conditions such as worsening heart failure events are described. A system may include sensor circuits to sense physiological signals and signal processors to generate from the physiological signals first and second signal metrics. The system may include a risk stratifier circuit to produce a cardiac risk indication. The system may use at least the first signal metric to generate a primary detection indication, and use at least the second signal metric and the risk indication to generate a secondary detection indication. The risk indication may be used to modulate the second signal metric. A detector circuit may detect the worsening cardiac event using the primary and secondary detection indications.

CLAIM OF PRIORITY

This application is a continuation of U.S. application Ser. No.16/853,421, filed Apr. 20, 2020, which is a continuation of U.S.application Ser. No. 15/473,783, filed Mar. 30, 2017, which claims thebenefit of priority under 35 U.S.C. § 119(e) of U.S. Provisional PatentApplication Ser. No. 62/316,905, filed on Apr. 1, 2016, which are hereinincorporated by reference in their entireties.

TECHNICAL FIELD

This document relates generally to medical devices, and moreparticularly, to systems, devices and methods for detecting andmonitoring events indicative of worsening of congestive heart failure.

BACKGROUND

Congestive heart failure (CHF or HF) is a major health problem andaffects many people in the United States alone. CHF patients may haveenlarged heart with weakened cardiac muscles, resulting in poor cardiacoutput of blood. Although CHF is usually a chronic condition, it mayoccur suddenly. It may affect the left heart, right heart or both sidesof the heart. If CHF affects the left ventricle, signals that controlthe left ventricular contraction are delayed, and the left and rightventricles do not contract simultaneously. Non-simultaneous contractionsof the left and right ventricles further decrease the pumping efficiencyof the heart.

In many CHF patients, elevated pulmonary vascular pressures may causefluid accumulation in the lungs over time. The fluid accumulation mayprecede or coincide with worsening of HF such as episodes of HFdecompensation. The HF decompensation may be characterized by pulmonaryor peripheral edema, reduced cardiac output, and symptoms such asfatigue, shortness of breath, and the like.

SUMMARY

Ambulatory medical devices may be used for monitoring HF patient anddetecting worsening cardiac conditions such as a worsening heart failure(WHF) event. Examples of such ambulatory medical devices may includeimplantable medical devices (IMD), subcutaneous medical devices,wearable medical devices or other external medical devices. Theambulatory medical devices may include physiological sensors which maybe configured to sense electrical activity and mechanical function ofthe heart. The ambulatory medical devices may deliver therapy such aselectrical stimulations to target tissues or organs, such as to restoreor improve the cardiac function. Some of these devices may providediagnostic features, such as using transthoracic impedance or othersensor signals to detect a disease or a disease condition. For example,fluid accumulation in the lungs decreases the transthoracic impedancedue to the lower resistivity of the fluid than air in the lungs.

Detection of worsening cardiac conditions may be based on a detectedchange of a sensor signal (such as a thoracic impedance signal) from areference signal. An ideal detector of worsening cardiac conditions,such as a WHF event, may have one or more of a high sensitivity, a highspecificity, a low false positive rate (FPR), or a high positivepredictive value (PPV). The sensitivity may be represented as apercentage of actual WHF events that are correctly recognized by adetection method. The specificity may be represented as a percentage ofactual non-WHF events that are correctly recognized as non-WHF events bythe detection method. The FPR may be represented as a frequency of falsepositive detections of WHF events per patient within a specified timeperiod (e.g., a year). The PPV may be represented as a percentage of thedetected WHF events, as declared by the detection method, which areactual WHF events. A high sensitivity may help ensure timelyintervention to a patient with an impending WHF episode, whereas a highspecificity and a high PPV may avoid unnecessary intervention and reducefalse alarms.

Frequent monitoring of CHF patients and timely and accurate detection ofWHF events may reduce cost associated with HF hospitalization. CHFpatients, however, may be exposed to different degrees of risks ofdeveloping a future WHF event. Therefore, identification of patients atrelatively higher risks may ensure more effective and timely treatment,improve the prognosis and patient outcome, and avoid unnecessary medicalintervention and reduce healthcare cost.

This document discusses, among other things, a patient management systemfor detecting worsening cardiac events such as WHF events that based atleast on identified patient risks of developing future WHF events. Thesystem discussed herein may include sensor circuits to sensephysiological signals and processors to generate from the physiologicalsignals first and second signal metrics. The system may include a riskstratifier circuit to produce a cardiac risk indication. The system mayuse at least the first signal metric to generate a primary detectionindication, and use at least the second signal metric and the riskindication to generate a secondary detection indication. The riskindication may be used to modulate the second signal metric. A detectorcircuit may detect the worsening cardiac event using the primary andsecondary detection indications.

In Example 1, a system for detecting a worsening cardiac event in apatient is disclosed. The system may comprise sensor circuits includingsense amplifier circuits to sense a first physiological signal and asecond physiological signal, a signal processor circuit configured togenerate a first signal metric from the first physiological signal and asecond signal metric from the second physiological signal, a riskstratifier circuit configured to produce a risk indication indicating arisk of the patient developing a future worsening cardiac event, and adetector circuit coupled to the signal processor circuit and the riskstratifier circuit. The detector circuit may be configured to generate aprimary detection indication using at least the first signal metric anda secondary detection indication using at least the second signal metricand the risk indication, and to detect the worsening cardiac event usingthe primary and secondary detection indications.

Example 2 may include, or may optionally be combined with the subjectmatter of Example 1 to optionally include, an output circuit that maygenerate an alert in response to the detection of the worsening cardiacevent.

Example 3 may include, or may optionally be combined with the subjectmatter of one or any combination of Examples 1 or 2 to include, thefirst signal metric that may include a heart sound signal metric and thesecond signal metric includes a respiratory signal metric. The heartsound signal metric may include a third heart sound (S3) intensity or aratio of a third heart sound (S3) intensity to a reference heart soundintensity, and the respiratory signal metric may include a respirationrate measurement, a tidal volume measurement, or a ratio of therespiration rate to the tidal volume measurement.

Example 4 may include, or may optionally be combined with the subjectmatter of one or any combination of Examples 1 through 3 to include, thedetector circuit that may detect the worsening cardiac event using adecision tree including the primary and secondary detection indications.The secondary detection indication may be generated based on asub-decision tree included in the decision tree. The sub-decision treemay include the risk indication and a detection based on at least thesecond signal metric.

Example 5 may include, or may optionally be combined with the subjectmatter of Example 4 to optionally include, the sensor circuits that mayfurther include a third sense amplifier circuit to sense a thirdphysiological signal and the sub-decision tree that may further includea detection based on the third physiological signal. The detectorcircuit may be configured to generate the secondary detection indicationusing the risk indication if the decision based on the secondphysiological signal indicates a detection of the worsening cardiacevent, or generate the secondary detection indication using thedetection based on the third physiological signal if the decision basedon the second physiological signal indicates no detection of theworsening cardiac event.

Example 6 may include, or may optionally be combined with the subjectmatter of one or any combination of Examples 1 through 5 to include, theprimary or secondary detection indication that may include aBoolean-logic or fuzzy-logic combination of two or more signal metrics,or the risk indication that may include a Boolean-logic or fuzzy-logiccombination of two or more risk indications.

Example 7 may include, or may optionally be combined with the subjectmatter of one or any combination of Examples 1 through 6 to include, thedetector circuit that may generate a composite signal trend using acombination of the first signal metric and the second signal metricmodulated by the risk indication, and detect the worsening cardiac eventin response to the composite signal trend satisfying a specifiedcondition.

Example 8 may include, or may optionally be combined with the subjectmatter of Example 7 to optionally include, the modulation of the secondsignal metric that may include a temporal change of the second signalmetric weighted by the risk indication.

Example 9 may include, or may optionally be combined with the subjectmatter of Example 7 to optionally include, the modulation of the secondsignal metric that may include a temporal change of the second signalmetric sampled when the risk indication satisfies a specified condition.

Example 10 may include, or may optionally be combined with the subjectmatter of one or any combination of Examples 1 through 9 to include, thesecond signal metric that is more sensitive and less specific to theworsening cardiac event than the first signal metric.

In Example 11, a system for identifying a patient's risk of developing afuture worsening cardiac disease is disclosed. The system may comprisesensor circuits, a signal processor circuit, a risk stratifier circuitcoupled to the signal processor circuit, and an output circuit. Thesensor circuits may include sense amplifier circuits to sense first,second, and third physiological signals. The signal processor circuitmay generate a first signal metric from the first physiological signal,a second signal metric from the second physiological signal, and a thirdsignal metric from the second physiological signal. The risk stratifiercircuit generate a primary cardiac risk indication using at least thefirst signal metric, a secondary cardiac risk indication using at leastthe second and third signal metrics, and a composite cardiac riskindication using both the primary and secondary cardiac riskindications. The output circuit may provide the composite cardiac riskindication to a clinician or a process.

Example 12 may include, or may optionally be combined with the subjectmatter of Example 11 to optionally include, the risk stratifier circuitthat may generate a secondary cardiac risk indication using a pluralityof measurements of the second signal metric during a time period whenthe third signal metric satisfies a specified condition.

Example 13 may include, or may optionally be combined with the subjectmatter of one or any combination of Examples 11 or 12 to include, thesignal processor circuit that may generate a first plurality ofmeasurements of the first signal metric and a second plurality ofmeasurements of the second signal metric. The risk stratifier circuitmay generate the primary cardiac risk indication including a firststatistic of the first plurality of measurements of the first signalmetric, and the secondary cardiac risk indication including a secondstatistic of the second plurality of measurements of the second signalmetric. The risk stratifier circuit may generate the composite cardiacrisk indication using a combination of the first statistic of the firstsignal metric and the second statistic of the second signal metric.

Example 14 may include, or may optionally be combined with the subjectmatter of one or any combination of Examples 11 through 13 to include, afusion model selector circuit that may select a fusion model from aplurality of candidate fusion models based on signal quality of thefirst, second, and third physiological signals. The risk stratifiercircuit may generate the composite cardiac risk indication using boththe primary and secondary cardiac risk indications according to theselected fusion model.

Example 15 may include, or may optionally be combined with the subjectmatter of one or any combination of Examples 11 through 14 to include,the risk stratifier circuit that may transform the composite cardiacrisk indication using a sigmoid function.

In Example 16, a method for detecting a worsening cardiac event in apatient is disclosed. The method may include steps of sensing, viasensor circuits, first and second physiological signals; generating afirst signal metric from the first physiological signal and a secondsignal metric from the second physiological signal; producing a riskindication indicating a risk of the patient developing a futureworsening cardiac event; generating a primary detection indication usingat least the first signal metric, and a secondary detection indicationusing at least the second signal metric and the risk indication; anddetecting the worsening cardiac event using the primary and secondarydetection indications.

Example 17 may include, or may optionally be combined with the subjectmatter of Example 16 to optionally include, the method of detecting theworsening cardiac event including using a decision tree based on theprimary and secondary detection indications. The decision tree mayinclude a sub-decision tree based on the risk indication and a detectionbased on at least the second signal metric.

Example 18 may include, or may optionally be combined with the subjectmatter of Example 16 to optionally include, the primary or secondarydetection indication that may include a Boolean-logic or fuzzy-logiccombination of two or more signal metrics, or the risk indicationincludes a Boolean-logic or fuzzy-logic combination of two or more riskindications.

Example 19 may include, or may optionally be combined with the subjectmatter of Example 16 to optionally include, steps of generating acomposite signal trend using a combination of the first signal metricand the second signal metric modulated by the risk indication, whereinthe worsening cardiac event is detected in response to the compositesignal trend satisfying a specified condition.

Example 20 may include, or may optionally be combined with the subjectmatter of Example 19 to optionally include, the modulation of the secondsignal metric that may include a scaled temporal change of the secondsignal metric weighted by the risk indication, or a sampled temporalchange of the second signal metric when the risk indication satisfies aspecified condition.

Example 21 may include, or may optionally be combined with the subjectmatter of Example 16 to optionally include, the method of producing therisk indication that may include generating a primary cardiac riskindication using at least a first signal metric for cardiac riskassessment and a secondary cardiac risk indication using at least secondand third signal metrics for cardiac risk assessment, and generating acomposite cardiac risk indication using both the primary and secondarycardiac risk indications.

Example 22 may include, or may optionally be combined with the subjectmatter of Example 21 to optionally include, the method of generating thesecondary cardiac risk indication which may include taking a pluralityof measurements of the second signal metric during a time period whenthe third signal metric satisfies a specified condition.

Example 23 may include, or may optionally be combined with the subjectmatter of Example 21 to optionally include, the method of producing therisk indication that may include transforming the composite cardiac riskindication using a sigmoid function.

The systems, devices, and methods discussed in this document may improvethe medical technology of automated monitoring of patients withworsening heart failure (WHF). The detection of WHF based on primary andsecondary detections and a cardiac risk indication may enhance theperformance and functionality of a medical system or an ambulatorymedical device for detecting WHF. In certain examples, the enhanceddevice functionality may include more timely detection of WHF withincreased accuracy (e.g., lower false positive rate and higher positivepredictive value) at little to no additional cost. The improvement insystem performance and functionality, provided by the present systemsand methods, can reduce healthcare costs associated with management andhospitalization of heart failure patients. The systems, devices, andmethods discussed in this document also allow for more efficient devicememory usage, such as by storing cardiac risk indications and signalmetrics that are clinically more relevant to WHF. As fewer falsepositive detections are provided, device battery life can be extended,fewer unnecessary drugs and procedures may be scheduled, prescribed, orprovided, and an overall system cost savings may be realized.

This Summary is an overview of some of the teachings of the presentapplication and not intended to be an exclusive or exhaustive treatmentof the present subject matter. Further details about the present subjectmatter are found in the detailed description and appended claims. Otheraspects of the invention will be apparent to persons skilled in the artupon reading and understanding the following detailed description andviewing the drawings that form a part thereof, each of which are not tobe taken in a limiting sense. The scope of the present invention isdefined by the appended claims and their legal equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are illustrated by way of example in the figures ofthe accompanying drawings. Such embodiments are demonstrative and notintended to be exhaustive or exclusive embodiments of the presentsubject matter.

FIG. 1 illustrates generally an example of a patient management systemand portions of an environment in which the patient management systemmay operate.

FIG. 2 illustrates generally an example of a cardiac event detectionsystem for detecting a worsening cardiac event.

FIGS. 3A-3D illustrate generally examples of secondary detectors forgenerating a secondary detection indication based at least on a secondsignal metric and the risk indication.

FIG. 4 illustrates generally an example of a risk stratifier circuit forassessing a patient risk of developing a future worsening cardiac event.

FIG. 5 illustrates generally an example of a secondary risk generatorfor generating a cardiac risk indication based on conditional samplingof a signal metric.

FIG. 6 illustrates generally an example of a method for detecting aworsening cardiac event.

FIGS. 7A-7B illustrate generally examples of decision trees fordetecting the worsening cardiac event.

FIG. 8 illustrates generally an example of a portion of a method fordetecting worsening cardiac event based at least the first and secondsignal metrics.

FIG. 9 illustrates generally an example of a method for cardiac riskassessment.

DETAILED DESCRIPTION

Disclosed herein are systems, devices, and methods for detectingworsening cardiac conditions, including events indicative of worseningheart failure. The WHF event may occur before systematic manifestationof worsening of HF. The systems, devices, and methods described hereinmay be used to determine a patient's cardiac status as well as to trackprogression of the cardiac condition such as worsening of a HF event.This system may also be used in the context of HF comorbidities andworsening chronic diseases such as pulmonary congestion, pneumonia, orrenal diseases, among others.

FIG. 1 illustrates generally an example of a patient management system100 and portions of an environment in which the patient managementsystem 100 may operate. The patient management system 100 may include anambulatory system 105 associated with a patient body 102, an externalsystem 125, and a telemetry link 115 providing for communication betweenthe ambulatory system 105 and the external system 125.

The ambulatory system 105 may include an ambulatory medical device (AMD)110 and a therapy delivery system such as a lead system 108. The AMD 110may include an implantable device that may be implanted within the body102 and coupled to a heart 101 via the lead system 108. Examples of theimplantable device may include, but are not limited to, pacemakers,pacemaker/defibrillators, cardiac resynchronization therapy (CRT)devices, cardiac remodeling control therapy (RCT) devices,neuromodulators, drug delivery devices, biological therapy devices,diagnostic devices, or patient monitors, among others. The AMD 110 mayalternatively or additionally include subcutaneously implanted devicessuch as a subcutaneous ICD or a subcutaneous diagnostic device, wearablemedical devices such as patch based sensing device, or other externalmonitoring or therapeutic medical devices such as a bedside monitor.

The lead system 108 may include one or more transvenously,subcutaneously, or non-invasively placed leads or catheters. Each leador catheter may include one or more electrodes for delivering pacing,cardioversion, defibrillation, neuromodulation, drug therapies, orbiological therapies, among other types of therapies. In an example, theelectrodes on the lead system 108 may be positioned inside or on asurface of at least a portion of the heart, such as a right atrium (RA),a right ventricle (RV), a left atrium (LA), a left ventricle (LV), orany tissue between or near the heart portions. The arrangements and usesof the lead system 108 and the associated electrodes may be determinedbased on the patient need and the capability of the AMD 110. In someexamples, the AMD 110 may include one or more un-tethered electrodesassociated with an outer surface of the AMD 110, and the AMD 110 and theassociated un-tethered electrodes may be configured to be deployed to atarget cardiac site or other tissue site.

The AMD 110 may house an electronic circuit for sensing a physiologicalsignal, such as by using a physiological sensor or the electrodesassociated with the lead system 108. Examples of the physiologicalsignal may include one or more of electrocardiogram, intracardiacelectrogram, arrhythmia, heart rate, heart rate variability,intrathoracic impedance, intracardiac impedance, arterial pressure,pulmonary artery pressure, left atrial pressure, RV pressure, LVcoronary pressure, coronary blood temperature, blood oxygen saturation,one or more heart sounds, intracardiac or endocardial acceleration,physical activity or exertion level, physiological response to activity,posture, respiration, body weight, or body temperature. The AMD 110 mayinitiate or adjust therapies based on the sensed physiological signals.

The patient management system 100 may include a worsening cardiac eventdetector circuit 160 provided for patient management using at leastdiagnostic data acquired by the ambulatory system 105. The worseningcardiac event detector circuit 160 may analyze the diagnostic data forpatient monitoring, risk stratification, and detection of events such asWHF or one or more HF comorbidities. In an example as illustrated inFIG. 1 , the worsening cardiac event detector circuit 160 may besubstantially included in the AMD 110. Alternatively, the worseningcardiac event detector circuit 160 may be substantially included in theexternal system 125, or be distributed between the ambulatory system 105and the external system 125.

The external system 125 may be used to program the AMD 110. The externalsystem 125 may include a programmer, a communicator, or a patientmanagement system that may access the ambulatory system 105 from aremote location and monitor patient status and/or adjust therapies. Byway of example and not limitation, and as illustrated in FIG. 1 , theexternal system 125 may include an external device 120 in proximity ofthe AMD 110, a remote device 124 in a location relatively distant fromthe AMD 110, and a telecommunication network 122 linking the externaldevice 120 and the remote device 124. The telemetry link 115 may be aninductive telemetry link, or a radio-frequency (RF) telemetry link. Thetelemetry link 115 may provide for data transmission from the AMD 110 tothe external system 125. This may include, for example, transmittingreal-time physiological data acquired by the AMD 110, extractingphysiological data acquired by and stored in the AMD 110, extractingpatient history data such as data indicative of occurrences ofarrhythmias, occurrences of decompensation, and therapy deliveriesrecorded in the AMD 110, and extracting data indicating an operationalstatus of the AMD 110 (e.g., battery status and lead impedance). Thetelemetry link 115 may also provide for data transmission from theexternal system 125 to the AMD 110. This may include, for example,programming the AMD 110 to perform one or more of acquiringphysiological data, performing at least one self-diagnostic test (suchas for a device operational status), delivering at least one therapy, oranalyzing data associated with patient health conditions such asprogression of heart failure.

Portions of the AMD 110 or the external system 125 may be implementedusing hardware, software, or any combination of hardware and software.Portions of the AMD 110 or the external system 125 may be implementedusing an application-specific circuit that may be constructed orconfigured to perform one or more particular functions, or may beimplemented using a general-purpose circuit that may be programmed orotherwise configured to perform one or more particular functions. Such ageneral-purpose circuit may include a microprocessor or a portionthereof, a microcontroller or a portion thereof, or a programmable logiccircuit, or a portion thereof. For example, a “comparator” may include,among other things, an electronic circuit comparator that may beconstructed to perform the specific function of a comparison between twosignals or the comparator may be implemented as a portion of ageneral-purpose circuit that may be driven by a code instructing aportion of the general-purpose circuit to perform a comparison betweenthe two signals.

FIG. 2 illustrates generally an example of a cardiac event detectionsystem 200 for detecting worsening cardiac conditions, such as a WHFevent. The cardiac event detection system 200 may include one or more ofsensor circuits 210, a signal processor circuit 220, a risk stratifiercircuit 230, a detector circuit 240, a controller circuit 250, and auser interface 260. In an example, a portion of the cardiac eventdetection system 200 may be implemented within the AMD 110, distributedbetween two or more implantable or wearable medical devices (such as animplantable medical device and a subcutaneous medical device), ordistributed between the AMD 110 and the external system 125.

The sensor circuits 210 may include at least a first sense amplifiercircuit 212 to sense a first physiological signal and a second senseamplifier circuit 214 to sense a different second physiological signal.The first and second physiological signals may each be indicative ofintrinsic physiological activities, evoked physiological activities whenthe heart or other tissues are stimulated in accordance with a specifiedstimulation configuration, or physiological activities under otherspecified conditions. The first or second sense amplifier circuit may becoupled to one or more electrodes such as on the lead system 108, or oneor more implantable, wearable, or other ambulatory physiologicalsensors, to sense the physiological signal(s). Examples of physiologicalsensors may include pressure sensors, flow sensors, impedance sensors,accelerometers, microphone sensors, respiration sensors, temperaturesensors, or blood chemical sensors, among others. Examples of thephysiological signals sensed by the sensor circuits 210 may includeelectrocardiograph (ECG), an electrogram (EGM), an intrathoracicimpedance signal, an intracardiac impedance signal, an arterial pressuresignal, a pulmonary artery pressure signal, a RV pressure signal, a LVcoronary pressure signal, a coronary blood temperature signal, a bloodoxygen saturation signal, central venous pH value, a heart sound (HS)signal, a posture signal, a physical activity signal, or a respirationsignal, among others. In some examples, the first or second senseamplifier may retrieve a respective physiological signal stored in astorage device such as an external programmer, an electronic medicalrecord (EMR) system, or a memory unit, among other storage devices.

The signal processor circuit 220, coupled to the physiological sensorcircuit 210, may include a first filter circuit 222 to filter the firstsensed physiological signal to produce a trend of a first signal metricX1_(D) for detection, and a second filter circuit 224 to filter thesecond sensed physiological signal to produce a trend of a second signalmetric X2_(D) for detection. The first and second signal metrics X1_(D)and X2_(D) may each include statistical parameters extracted from thesensed physiological signal, such as signal mean, median, or othercentral tendency measures or a histogram of the signal intensity, amongothers. The first and second signal metrics may additionally oralternatively include morphological parameters such as maximum orminimum within a specified time period such as a cardiac cycle, aspecific posture or an activity intensity, positive or negative slope orhigher order statistics, or signal power spectral density at a specifiedfrequency range, among other morphological parameters.

Depending on the respective sensed physiological signal, various firstand second signal metrics may be generated. In an example, a thoracic orcardiac impedance signal may be sensed using the electrodes on the leadsystem 108, and impedance metrics may include thoracic impedancemagnitude within a specified frequency range obtained from. In anexample, a heart sound (HS) signal may be sensed from an accelerometer,a microphone, or an acoustic sensor coupled to the AMD 110, and HSmetrics may include intensities of first (S1), second (S2), third (S3),or fourth (S4) heart sound components or a relative intensity such as aratio between two heart sound components, timing of one of the S1, S2,S3, or S4 heart sound components relative to a fiducial point such as aP wave, Q wave, or R wave in an ECG. In an example, the accelerometermay be associated with a lead such as of the lead system 108 or on asurface of an intracardiac pacing device located inside the heart. Theaccelerometer may be configured to sense intracardiac or endocardialaccelerations indicative of heart sounds. In an example, a respirationsignal may be sensed using an impedance sensor or an accelerometer, andthe respiratory metric may include a respiratory rate, a tidal volume, aminute ventilation, a posture, or a rapid-shallow breathing index (RSBI)computed as a ratio of a respiratory rate measurement to a tidal volumemeasurement. In another example, a physical activity signal may besensed using an accelerometer, and the activity metrics may includephysical activity intensity, or a time duration when the activityintensity is within a specified range or above a specified threshold. Inyet another example, a blood pressure signal may be sensed using apressure sensor, and the pressure metrics may include systolic bloodpressure, diastolic blood pressure, mean arterial pressure, and thetiming metrics of these pressure measurements with respect to a fiducialpoint.

In an example, the second signal metric X2_(D) may differ from the firstsignal metric X1_(D) such that X2_(D) may be more sensitive and lessspecific to a worsening cardiac event (such as a WHF event) than X1_(D).Relative sensitivity or specificity may be based on detectionperformance of the signal metrics across a cohort of patients. In anexample, the second signal metric X2_(D) may be evaluated when the firstsignal metric X1_(D) does not indicate a detection of worsening cardiacevent. A more sensitive X2_(D) may be used to reduce the false negativedetections of the worsening cardiac event based solely on X1_(D). In anexample, the first signal metric X1_(D) may include a HS metric such asa S3 heart sound intensity or a ratio of S3 intensity to a HS referenceintensity. Examples of the reference intensity may include a first heartsound (S1) intensity, a second heart sound (S2) intensity, or heartsound energy during a specified time period within a cardiac cycle.Other examples of the second signal metric X2_(D) may include thoracicimpedance magnitude, or respiratory metric such as respiratory ratemeasurement, a minute ventilation measurement, a tidal volumemeasurement, or an RSBI.

A signal metric trend may be formed using multiple measurements of thesignal metric during a specified time period. In an example, the signalmetric trend may include a daily trend including daily measurement of asignal metric over a specified number of days. Each daily measurementmay be determined as a central tendency of a plurality of measurementsobtained within a day. In an example, a thoracic impedance trend may begenerated using portions of the received impedance signal duringidentical phases of a cardiac cycle such as within a certain time windowrelative to R-wave in a ECG signal), or at identical phases of arespiratory cycle such as within an inspiration phase or an expirationphase of a respiration signal. This may minimize or attenuate theinterferences such as due to cardiac or respiratory activities, in theimpedance measurements. The thoracic impedance trend may be generatedusing impedance measurements collected during one or more impedanceacquisition and analysis sessions. In an example, an impedanceacquisition and analysis session may start between approximately 5 a.m.and 9 a.m. in the morning, and lasts for approximately 2-8 hours. Inanother example, the impedance acquisition and analysis session may beprogrammed to exclude certain time periods, such as night time, or whenthe patient is asleep. The impedance parameter may be determined as amedian of multiple impedance measurements acquired during the impedanceacquisition and analysis session.

The risk stratifier circuit 230 may produce a risk indication (R)indicating a risk of the patient developing a future worsening cardiacevent. The risk indication may have categorical values indicating riskdegrees such as “high”, “medium”, or “low” risks, or alternativelynumerical risk scores within a specified range. The risk scores may havediscrete values (e.g., integers from 0 through 5) or continuous values(e.g., real numbers between 0 and 1), where a larger risk scoreindicates a higher risk.

In an example, the risk indication may be at least partiallyautomatically retrieved from a memory that stores the patient'sup-to-date risk information. In an example, the risk stratifier circuit230 may determine the risk indication by analyzing a physiologicalsignal, such as by using one or more signal metrics generated by thesignal processor circuit 220 from the physiological signal. Thephysiological signal or the signal metrics (denoted by X1_(R), X2_(R),etc.) for assessing cardiac risk may be different from the physiologicalsignals or the signal metrics used for detecting the cardiac event (suchas the first and second signal metrics X1_(D) and X2_(D) generated atthe first and second filters 222 and 224). In another example, at leastone signal metric may be used for both cardiac risk assessment and forcardiac event detection. By way of non-limiting examples, the signalmetrics for cardiac risk assessment may include intensity of a heartsound component such as S3 heart sound, a respiratory rate, a tidalvolume measurement, a thoracic impedance magnitude, or physical activityintensity, among others. The risk indication generated by the riskstratifier circuit 230 may be confirmed or edited by a system user suchas via the user interface 260. Examples of the risk stratifier circuitfor assessing a cardiac risk are discussed below, such as with referenceto FIGS. 4-5 .

In some examples, the risk stratifier circuit 230 may determine the riskindication using at least information about patient's overall healthconditions, clinical assessments, or other current and historic diseasesstates that may increase or decrease the patient's susceptibility tofuture WHF. For example, following a WHF event, a patient may have anelevated risk of developing another WHF event or being re-hospitalized.The risk stratifier circuit 230 may determine the risk indication usingtime elapsed since the last WHF event. In another example, a patienthaving a medical history of atrial fibrillation may be more susceptibleto a future WHF event. The risk stratifier circuit 230 may determine therisk indication using a trend consisting of the time spent in AF eachday. In another example, the risk indication may be determined based onthe number or severity of one or more comorbid conditions, such as HFcomorbidities.

The detector circuit 240 may be coupled to the signal processor circuit220 and the risk stratifier circuit 230 to detect a worsening cardiacevent, such as a WHF event. The detector circuit 240 may be implementedas a part of a microprocessor circuit. The microprocessor circuit may bea dedicated processor such as a digital signal processor, applicationspecific integrated circuit (ASIC), microprocessor, or other type ofprocessor for processing information including the physiological signalsreceived from the sensor circuits 210. Alternatively, the microprocessorcircuit may be a general purpose processor that may receive and executea set of instructions of performing the functions, methods, ortechniques described herein.

The detector circuit 240 may include circuit sets comprising one or moreother circuits or sub-circuits, such as a primary detector circuit 242,a secondary detector circuit 244, and a detection fusion circuit 246, asillustrated in FIG. 2 . These circuits may, alone or in combination,perform the functions, methods, or techniques described herein. In anexample, hardware of the circuit set may be immutably designed to carryout a specific operation (e.g., hardwired). In an example, the hardwareof the circuit set may include variably connected physical components(e.g., execution units, transistors, simple circuits, etc.) including acomputer readable medium physically modified (e.g., magnetically,electrically, moveable placement of invariant massed particles, etc.) toencode instructions of the specific operation. In connecting thephysical components, the underlying electrical properties of a hardwareconstituent are changed, for example, from an insulator to a conductoror vice versa. The instructions enable embedded hardware (e.g., theexecution units or a loading mechanism) to create members of the circuitset in hardware via the variable connections to carry out portions ofthe specific operation when in operation. Accordingly, the computerreadable medium is communicatively coupled to the other components ofthe circuit set member when the device is operating. In an example, anyof the physical components may be used in more than one member of morethan one circuit set. For example, under operation, execution units maybe used in a first circuit of a first circuit set at one point in timeand reused by a second circuit in the first circuit set, or by a thirdcircuit in a second circuit set at a different time.

The primary detector 242 may generate a primary detection indication D1using at least the first signal metric X1_(D). The detection may bebased on temporal change of the first signal metric X1_(D), such as arelative difference of the signal metric from a reference levelrepresenting a signal metric baseline. In an example, the relativedifference may be calculated as a difference between a central tendencyof multiple measurements of X1_(D) within a short-term window and acentral tendency of multiple measurements of X1_(D) within a long-termwindow preceding the short-term time window in time. The relativedifference may be compared to a specified condition (e.g., a thresholdor a specified range), and generate a binary primary detectionindication D1 of “1” if the relative difference satisfies the specifiedcondition, or “0” if the relative fails to satisfy the specifiedcondition. In lieu of binary detection indications, the primary detector242 may alternatively produce the detection indication D1 having realnumbers (such as between 0 and 1) indicative of confidence of detection.The confidence may be proportional to the deviation of the signal metricdifference (e.g., ΔX1_(C)) from a detection threshold.

The secondary detector 244 may generate a secondary detection indicationD2 using at least the second signal metric X2_(D)) and the riskindication R. In an example, the secondary detector 244 may calculate arelative difference (ΔX₂) between a representative value of the secondsignal metric X2_(D) such as a central tendency of multiple measurementsof X2_(D) within a short-term window and a baseline value such as acentral tendency of multiple measurements of X2_(D) within a long-termwindow preceding the short-term window in time. The secondary detector244 may compute the secondary detection indication D2 using a linear,nonlinear, or logical combination of the relative difference (ΔX₂) andthe risk indication R. The relative difference (ΔX₂) may be modulated bythe risk indications R. Similar to the primary detection indication D1,the secondary detection indication D2 may have a discrete value such as“0” indicating no detection and a “1” indicating detection of theworsening cardiac event based on ΔX₂, or continuous values within aspecified range such as indicating the confidence of the detection.Examples of the secondary detector using the second signal metric X2_(D)and the risk indication R are discussed below, such as with reference toFIGS. 3A-3D.

The detection fusion circuit 246 may generate a composite detectionindication (CDI) using the primary detection indication D1 and thesecondary detection indication D2. In an example, the detection fusioncircuit 246 may generate the CDI using a decision tree. The decisiontree may be implemented as a set of circuits, such as logic circuit,that perform logical combinations of at least the primary and secondarydetection indications D1 and D2. Alternatively, at least a portion ofthe decision tree may be implemented in a microprocessor circuit, suchas a digital signal processor or a general purpose processor, which mayreceive and execute a set of instructions including logical combinationsof at least the primary and secondary detection indications D1 and D2.

The decision tree for detecting the worsening cardiac event may includea tiered detection process comprising the primary detection indicationD1, and subsequent detection indication D2 if the primary detectionindication D1 indicates no detection of the worsening cardiac event. Inan example, according to the decision tree, the CDI may be expressed asBoolean logic “OR” between D1 and D2 each satisfying respectiveconditions, as shown in Equation (1):

CDI=(D1) OR (D2)  (1)

In an example, D1 is based on a heart sound metric of a ratio of S3 toS1 heart sound intensity (S3/S1), and D2 is based on a metric ofthoracic impedance magnitude (Z) or a rapid-shallow breathing index(RSBI).

As to be discussed below with reference to FIGS. 3A-3D, the secondarydetection indication D2 may be generated as a logical or linearcombinations of the second signal metric X2_(D) and the risk indicationR. In an example, the logical combination of the risk indication (R) andthe second signal metric X2_(D) may be represented by a sub-decisiontree included in the decision tree for detecting the worsening cardiacevent. In an example, the risk indication is evaluated only when thesecond signal metric X2_(D) indicates a detection of the worseningcardiac event (such as when S2_(D) satisfies a detection condition).Accordingly, the secondary detection indication D2 may be represented asBoolean logic “AND” between X2_(D) and R, that is, D2=X2_(D) AND R.Substituting the logical formula of D2 into Equation (1) yields Equation(2) corresponding to the decision tree that includes the sub-decisiontree for determining D2:

CDI=(X1_(D)) OR ((X2_(D)) AND (R))  (2)

In an example, the second signal metric X2_(D) includes the thoracicimpedance (Z) and the risk indication (R) is assessed using S3 heartsound, such as a central tendency or variability of S3 intensitymeasurements. The CDI for detecting the worsening cardiac event may beexpressed as in Equation (3) below, where T1, T2 and T3 denotethresholds for the respective signal metrics:

$\begin{matrix}{{CDI} = \left\{ \begin{matrix}{1,{{if}\left( {\frac{S3}{S1} > {T1}} \right)\ {OR}\ \left( {\left( {Z > {T2}} \right)\ {AND}\ \left( {{S3} > {T3}} \right)} \right)}} \\{0,\ {else}}\end{matrix} \right.} & (3)\end{matrix}$

In addition to or in lieu of the decision tree, the detection circuit240 may generate the CDI from a composite signal trend (cY) such as alinear or a nonlinear combination of the relative difference of thefirst signal metric X1_(D), and the relative difference of the secondsignal metric X2_(D) modulated by the risk indications R. Examples ofmodulation of second signal metric may include scaling the second signalmetric X2_(D) by the risk indication R, or sampling X2_(D) conditionallyupon the risk indication R satisfying a specified condition. Modulationssuch as scaling and conditional sampling of X2_(D) are discussed belowwith reference to FIGS. 3A-3B.

To account for differences in signal properties (such as signal range orsignal change or rate of change) of various signal metrics, the signalmetrics may be transformed into a unified scale such that they may beeasily comparable or combined. In an example, the primary detector 242may transform the relative difference of X1_(D) into a first sequence oftransformed indices Y1=f₁(X1_(D)). The secondary detector 244 maysimilarly transform the relative difference of X2_(D)) into a secondsequence of transformed indices Y2=f₂(X2_(D)) within the same specifiedrange. In an example, the transformations f₁ and f₂ may each include ause of respective codebook that maps quantized magnitude of respectivesignal metric into numerical indices within a specified range, where alarger code indicates a higher signal magnitude. In an example, thetransformed indices Y1 or Y2 may be obtained from a transformation oflinear or nonlinear combination of more than one signal metrics.

The secondary detector 244 may modulate the transformed indices Y2 bythe risk indication R, denoted by Y2_(R), and the detection fusioncircuit 246 may generate the composite signal trend cY by combining Y1and Y2_(R), such as a linear combination as shown in Equation (4) below:

cY=Y1+Y2|_(R)  (4)

In an example, the modulation includes a multiplication operationbetween Y2 and R. In another example, the modulation includesconditionally-sampling of Y2 upon R satisfying a specified condition.Examples of the secondary detector using the second signal metric X2_(D)and the risk indication R are discussed below, such as with reference toFIGS. 3A-3D. The detection fusion circuit 246 may determine the CDI bycomparing the composite signal trend cY to a threshold, as shown inEquation (5) below, where T1 denotes the threshold for cY:

$\begin{matrix}{{CDI} = \left\{ \begin{matrix}{1,{{{if}\left( {{{Y1} + {Y2}}❘_{R}} \right)} > {T1}}} \\{0,{else}}\end{matrix} \right.} & (5)\end{matrix}$

The controller circuit 250 may control the operations of the sensorcircuits 210, the signal processor circuit 220, the risk stratifiercircuit 230, the detector circuit 240, the user interface unit 260, andthe data and instruction flow between these components. In an example aspreviously discussed, the controller circuit 250 may configure theoperations of the secondary detector 243, such as a combination of thesecond signal metric and the risk indication for generating thesecondary detection indication D2.

The user interface 260 may include a user input module 261 and an outputmodule 261. In an example, at least a portion of the user interface unit260 may be implemented in the external system 120. The user input module261 may be coupled to one or more user input device such as a keyboard,on-screen keyboard, mouse, trackball, touchpad, touch-screen, or otherpointing or navigating devices. The input device may enables a systemuser (such as a clinician) to program the parameters used for sensingthe physiological signals, assessing risk indications, and detectingworsening cardiac event. The output module 262 may generate ahuman-perceptible presentation of the composite detection indication(CDI), such as displayed on the display. The presentation may includeother diagnostic information including the physiological signals and thesignals metrics, the primary and secondary detection indications, therisk indications, as well as device status such as lead impedance andintegrity, battery status such as remaining lifetime of the battery, orcardiac capture threshold, among others. The information may bepresented in a table, a chart, a diagram, or any other types of textual,tabular, or graphical presentation formats, for displaying to a systemuser. Additionally or alternatively, the CDI may be presented to theprocess such as an alert circuit for producing an alert in response tothe CDI satisfies a specified condition. The alert may include audio orother human-perceptible media format.

In some examples, the cardiac event detection system 200 mayadditionally include a therapy circuit 270 configured to deliver atherapy to the patient in response to one or more of the primary orsecondary detection indications or the composite detection indication.Examples of the therapy may include electrostimulation therapy deliveredto the heart, a nerve tissue, other target tissues in response to thedetection of the target physiological event, or drug therapy includingdelivering drug to a tissue or organ. In some examples, the primary orsecondary detection indications or the composite detection indicationmay be used to modify an existing therapy, such as adjusting astimulation parameter or drug dosage.

FIGS. 3A-3D illustrate generally examples of secondary detectors 310,320, 330 and 340 for generating a secondary detection indication (D2)based at least on a second signal metric X2_(D) such as produced at thesecond filter 224 and the risk indication (R) such as produced at therisk stratifier circuit 230. The secondary detectors 310, 320, 330 and340 may be embodiments of the secondary detector 244 in FIG. 2 . Thesecondary detection indication D2 may be a linear or a nonlinearcombination of the temporal change of a second signal metric X2_(D) andthe risk indication R. In an example as illustrated in FIG. 3A, thesecondary detector 310 may include a multiplier circuit 312 thatmultiplies the temporal change by the risk indication R to produce thesecondary detection indication D2. In an example, the risk indication Rmay take binary values “0” or “1”, such as to gate the contribution ofthe second signal metric X2_(D) to the secondary detection indication D2(e.g., using R to turn on the D2 if R=1, or to turn off D2 if R=0). Inanother example, the risk indication R may take real numbers such asbetween 0 and 1, such as to weight the contribution of the second signalmetric X2_(D) to the secondary detection indication D2. In an example,the risk indication from the risk stratifier circuit 230 includes asignal metric trend to modulate the second signal metric X2_(D) or atransformation of a temporal change of X2_(D), such as Y2_(R) as shownin Equation (4). The multiplier circuit 312 may produce a modulatedsecond signal metric (such as Y2*R), which would be used by thedetection fusion circuit 246 for generating the composite signal trendfor detecting worsening cardiac event.

FIG. 3B illustrates the secondary detector 320 that may generate thesecondary detection indication D2 using the second signal metric X2_(D)when the risk indication satisfies a specified condition. The secondarydetector 320 may include a sampling circuit 322, a comparator 324, and aconditional detector 326. The comparator 324 may compare the riskindication R to specified condition such as a specified range. Thesampling circuit 322 may sample the second signal metric X2_(D) onlywhen the risk indication R satisfies the specified condition, such aswhen the signal metric used for risk assessment falls within a specifiedrange. In an example, the second signal metric X2_(D) may include arespiratory rate trend, and the risk indication may include physicalactivity intensity. The sampling circuit 322 may sample the respiratoryrate trend during a time period when the physical activity intensityexceeds a specified threshold. The conditional detector 326 may generatethe secondary detection indication D2 using a statistical measure, suchas a central tendency or a variability, of the sampled RR measurements.In an example, the risk indication from the risk stratifier circuit 230includes a signal metric trend to modulate the second signal metricX2_(D) or a transformation of a temporal change of X2_(D), such asY2_(R) as shown in Equation (4). The sampling circuit 322 may produce amodulated second signal metric, including the conditionally sampledX2_(D) or conditionally sampled transformed signal metric Y2 upon Rsatisfying a specified condition. The conditionally sampled X2_(D) or Y2would be used by the detection fusion circuit 246 for generating thecomposite signal trend for detecting worsening cardiac event.

FIG. 3C illustrates the secondary detector 330 that may generate thesecondary detection indication D2 using a logical combination of thesecond signal metric X2_(D) and the risk indication R, such as thesub-decision tree included in the decision tree for detecting theworsening cardiac event, as previously discussed with reference to FIG.2 . The secondary detector 330 may include a comparator 332 to comparethe temporal change of the second signal metric X2_(D) to a threshold, acomparator 334 to compare the risk indication R to a threshold, and alogical combination circuit 336 to generate a detection decision basedon the sub-decision tree. In an example, if the second signal metricX2_(D) indicates a detection of the worsening cardiac event (such asfalling with a range), the logical combination circuit 336 may use therisk indication R to confirm the detection based on X2_(D). In someexamples, the secondary detector 330 may additionally receive a thirdsignal metric X3_(D) generated from the same or a differentphysiological signal from which X2_(D) is generated. The sub-decisiontree may additionally include the detection according to the thirdsignal metric X3_(D). If the second signal metric X2_(D) indicates nodetection of the worsening cardiac event, the logical combinationcircuit 336 may use X3_(D) to generate the secondary detectionindication D2.

FIG. 3D illustrates the secondary detector 340 that may generate thesecondary detection indication D2 using a fuzzy-logic combination of thesecond signal metric X2_(D) and the risk indication R. Compared to theBoolean logic which takes crisp decisions of “1” or “0” (such as basedon threshold crossing), the fuzzy-logic may take real numbers such asbetween 0 and 1. The fuzzifier circuit 342 may partition the range ofthe signal metric X2_(D) and the range of risk indication R each intorespective fuzzy sets, and to transform the second signal metric X2_(D)and the risk indication R each into respective fuzzified representationsX2_(D)′ and R′. The fuzzified presentations X2_(D)′ and R′ may then becombined using fuzzy-logic operators, including “minimum” ormultiplication operator corresponding to the Boolean operator “AND”,“maximum” or addition operator corresponding to the Boolean operator“OR”, and “1-x” (where x represents a fuzzified representation)corresponding to the Boolean operator “NOT”. In an example, thefuzzy-logic combination circuit 342 may compute a numerical D2 as“minimum” between the risk indication (R) and the second signal metricX2_(D), that is, D2=min(X2_(D), R), which corresponds to D2=X2_(D) AND Rin Boolean-logic combination as in the secondary detector 330.

In an example, the fuzzy-logic combination circuit 342 may combine thefuzzified presentations X2_(D)′ and R′ using a hybrid of the Booleanlogic and fuzzy-logic combinations. For example, the sub-decision treeas discussed in secondary detector 330 may include a Boolean-logiccombination, such that D2=(X2_(D)′) AND (R′), while the X2_(D)′ or R′may each be determined as fuzzy-logic combinations of two or more signalmetrics. For example, X2_(D)′ may be determined as a maximum between atemporal change of thoracic impedance (ΔZ) and a temporal change of RSBI(ΔRSBI), that is, X2_(D)′=max(Z, RSBI). In an example, R′ may bedetermined as a minimum of a central tendency or variability of S3intensity measurements S3, and the respiratory rate (RR) variability,that is, R′=min (S3, RR). By substituting the fuzzy-logicrepresentations of X2_(D)′ and R′ into the Boolean-logic representationof D2, the secondary detection indication D2 may be determined accordingto Equation (6) below:

D2=(max(Z,RSBI)>T1) AND (min(S3,RR)>T2)  (6)

FIG. 4 illustrates generally an example of a risk stratifier circuit 400for assessing a patient risk of developing a future worsening cardiacevent, such as a WHF event. The risk stratifier circuit 400 may be anembodiment of the risk stratifier circuit 230. The risk stratifiercircuit 400 may include one or more of a primary risk generator 410, asecondary risk generator circuit 420, an optional indication-based riskadjuster 440, and a blending circuit 430. The primary risk generator 410may be coupled to the signal processor circuit 220 to receive aplurality of measurements of a first signal metric 221 (X1_(R)) forcardiac risk assessment, and generate a primary cardiac risk indication(R1) using at least X1_(R). The signal metric X1_(R) may be differentfrom the first and second signal metrics X1_(D) and X2_(D) used by theprimary and secondary detectors 242 and 244 for detecting worseningcardiac event. In an example, the first signal metric X1_(R) may beextracted from a heart sound signal, and include one of a S3 intensity,or a ratio of a S3 intensity to a reference heart sound intensity suchas S1 intensity, S2 intensity, or heart sound energy during a specifiedportion of the cardiac cycle. The primary risk generator 410 maygenerate the primary cardiac risk indication (R1) using a statisticalmeasure, such as a central tendency or a variability, of the pluralityof the measurements of the signal metric X1_(R).

The secondary risk generator 420 may generate a secondary cardiac riskindication (R2) using a plurality of measurements of a second signalmetric 222 (X2_(R)) and a plurality of measurements of a third signalmetric 223 (X3_(R)) for cardiac risk assessment, such as generated bythe signal processor circuit 220. The signal metrics X2_(R) and X3_(R)may be different from the signal metric X1_(R) for cardiac riskassessment, and may be different from the signal metrics X1_(D) andX2_(D) for detecting worsening cardiac event. In an example, the secondsignal metric X2_(R) for cardiac risk assessment may include arespiration signal metric, such as a respiratory rate, a tidal volume,or a rapid-shallow breathing index (RSBI) computed as a ratio of therespiratory rate to the tidal volume. A patient who breathes rapidly(high respiratory rate) and shallowly (low tidal volume) tends to have ahigh RSBI. Other examples of X2_(R) may include thoracic impedancemagnitude indicating thoracic fluid accumulation. Examples of the thirdsignal metric X3_(R) for cardiac risk assessment may include physicalactivity intensity, or a time duration when the physical activityintensity satisfies a specified condition such as above a threshold.

The secondary risk generator 420 may generate the secondary cardiac riskindication (R2) using methods similar to those used by the secondarydetector 244 for generating the secondary detection indication D2 aspreviously discussed with reference to FIG. 2 . For example, similar tothe secondary detector 244 that take as input at least the second signalmetric X2_(D) and the risk indication R, the secondary risk generator420 takes as input at least the second and third cardiac signal metricsX2_(R) and X3_(R) to generate the secondary cardiac risk indication(R2). In an example, R2 may be a weighted combination of the second andthird cardiac signal metrics X2_(R) and X3_(R). In an example, R2 may bea nonlinear combination of X2_(R) and X3_(R), such as the second signalmetric X2_(R) weighted by the third signal metric X3_(R). In anotherexample, the secondary cardiac risk indication R2 may be determinedusing the second signal metric X2_(R) measured during a time period whenthe third signal metric X3_(R) satisfies a specified condition. Examplesof sampling the second signal metric X2_(R) conditional upon the thirdsignal metric X3_(R) are discussed below, such as with reference to FIG.5 . The secondary cardiac risk indication (R2) may be computed as astatistical measure, such as a central tendency or a variability, of thelinearly or nonlinearly combined X2_(R) and X3_(R), or from theconditionally sampled X2_(R) upon X3_(R) satisfying a specifiedcondition.

The blending circuit 430 may combine the primary and secondary riskindications R1 and R2 to generate a composite cardiac risk indication(cR), such as according to a fusion model. A fusion model may includeone or more signal metrics and an algorithm for computing a riskindication from the one or more signal metrics. Examples of the fusionmodels may include a linear weighted combination, a nonlinearcombination such as a decision tree, a neural network, a fuzzy-logicmodel, or a multivariate regression model, among others. The blendingcircuit 430 may generate the composite cardiac risk indication cR usinga first statistic of a plurality of measurements of the signal metricX1_(R) and a second statistic of a plurality of measurements of thecombined metric between X2_(R) and X3_(R). Examples of the first andsecond statistics may each include a first-order statistic such as acentral tendency measure or a second-order statistic such as avariability measure. In an example, the primary cardiac risk indicationR1 includes a central tendency of a plurality of measurements of thesignal metric X1_(R), and the secondary cardiac risk indication R2includes a variability of a plurality of measurements of the linearly ornonlinearly combined metric between X2_(R) and X3_(R) or conditionallysampled X2_(R). The blending circuit 430 may generate the compositecardiac risk indication cR by combining the central tendency of X1_(R)and the variability of X2_(R) or the variability of the combined X2_(R)and X3_(R). In another example, the blending circuit 430 may generatethe composite cardiac risk indication cR by combining the centraltendency of X1_(R) and the central tendency of X2_(R) or the centraltendency of the combined X2_(R) and X3_(R).

The risk stratifier circuit 400 may include a transformation circuit totransform the cR such as to be within a specified range (e.g., between 0and 1). The transformation may include a linear function, a piecewiselinear function, or a nonlinear function. By way of non-limitingexample, the transformation circuit may transform the cR using a sigmoidfunction, such as provided by Equation (7):

cR=1/(1+exp(−k*cR+b))  (7)

where “exp” denotes the exponential function, “k” is a positive number,and “b” is scalar.

In some examples, the risk stratifier circuit 400 may include a fusionmodel selector circuit that may select a fusion model from a pluralityof candidate fusion models, and the blending circuit 430 may generatethe composite cardiac risk indication cR according to the selectedfusion model. The fusion model selection may be based on signal qualityof the one or more physiological signals from which the cardiac signalmetrics X1_(R), X2_(R), or X3_(R) are generated. In an example, betweena first candidate fusion model that employs a respiration signal metricand a second candidate fusion model that employs a thoracic impedancesignal metric, if the respiration signal has a poor signal-to-noiseratio (SNR) or excessive variability compared to a specified signalquality criterion, or substantially out of a specified value range, thenthe blending circuit 430 may switch to a the second fusion modelutilizing the thoracic impedance signal metric for combining the primaryand secondary risk indications.

The optional indication-based risk adjuster 440 may adjust the cardiacrisk indications R1 or R2 according to information about the patientclinical indications. The clinical indications may include patientmedical history such as historical cardiac events, heart failurecomorbidities or other concomitant disease states, exacerbation ofrecent chronic disease, a previous medical procedure, a clinical labtest result, patient medication intake or other treatment undertaken,patient physical assessment, or patient demographics such as age,gender, race, or ethnicity. The clinical indications may be provided bya clinician such as via the user interface 260, or stored in a memorysuch as an electronic medical record (EMR) system. The blending circuit430 may generate the composite cardiac risk indication further using thepatient's clinical information about the patient. In an example, thecomposite cardiac risk indication cR may be adjusted by the cliniciansuch as via the user interface 260 according to the patient's clinicalindications.

In some examples, the patient clinical indications may have time-varyingeffect on the patient risk of developing a future disease. For example,a more recent disease state or a surgery may put the patient at higherrisk for developing a future worsening cardiac disease than a moreremote historical disease in patient medical history. To account for thetime-varying effect of the historical medical event, in an example, theindication-based risk adjuster 440 may produce time-varying weightfactors decaying with time elapsed from a historical medical event, andapply the time-varying weight factors to at least one of the primary orsecondary risk indications R1 or R2. The time-varying weight factor mayfollow a linear, exponential, or other nonlinear decay function of thetime elapsed from a historical medical event. In another example, theblending circuit 430 may adjust at least one of R1 or R2 temporarily.For example, the indication-based risk adjuster 440 may be configured tomaintain elevated risks of R1 or R2 above a baseline risk score within aspecified timeframe following a historical medical event, and resume tothe baseline risk score beyond the specified timeframe. The compositerisk indication cR may be used by the secondary detector 244 to generatethe second detection indication D2, as previously discussed withreference to FIG. 2 .

FIG. 5 illustrates generally an example of a secondary risk generator520 for generating a cardiac risk indication based on conditionalsampling of a signal metric. The secondary risk generator 520, which isan embodiment of the secondary risk generator 420 of FIG. 4 , mayinclude a sampling circuit 522 to receive a set of measurements of thesecond cardiac signal metric 222 (X2_(R)) from the signal processorcircuit 220. The secondary risk generator 520 may include a comparator524 to compare the third cardiac signal metric 223 (X3_(R)) to aspecified threshold. The sampling circuit 522 may sample themeasurements of X2_(R) when the third signal metric X3_(R) satisfies aspecified condition. In an example, the second cardiac signal metricX2_(R) may include a respiratory rate and the third cardiac signalmetric X3_(R) may include physical activity intensity or the duration ofthe physical activity above a threshold. The sampling circuit 522 maysample the respiratory rate measurements during a time period when ahigh physical activity is indicated, such as when the physical activityintensity exceeds a specified threshold. The conditional risk generator526 may generate the secondary cardiac risk indication (R2) using astatistical measure, such as a central tendency or a variability, of thesampled respiratory rate measurements produced by the sampling circuit522.

FIG. 6 illustrates generally an example of a method 600 for detecting aworsening cardiac event. The worsening cardiac event may include eventsindicative of progression of cardiac condition, such as a WHF event or aHF decompensation event. The method 600 may be implemented and operatein an ambulatory medical device such as an implantable or wearablemedical device, or in a remote patient management system. In an example,the method 600 may be executed by the worsening cardiac event detector160 or any embodiment thereof, or by the external system 125.

The method 600 begins at 610 by sensing first and second physiologicalsignals from a patient. Examples of the physiological signals mayinclude electrocardiograph (ECG), an electrogram (EGM), an intrathoracicimpedance signal, an intracardiac impedance signal, an arterial pressuresignal, a pulmonary artery pressure signal, a RV pressure signal, a LVcoronary pressure signal, a coronary blood temperature signal, a bloodoxygen saturation signal, central venous pH value, a heart sound (HS)signal, a posture signal, a physical activity signal, or a respirationsignal, among others.

At 620, at least a first signal metric may be generated from the firstphysiological signal and a second signal metric may be generated fromthe second physiological signal. The signal metric may includestatistical or morphological parameters extracted from the sensedphysiological signal. Examples of the signal metrics may includethoracic impedance magnitude, HS metrics such as intensities of S1, S2,S3, or S4 heart sounds or a relative intensity such as a ratio betweentwo heart sound components, a ratio of S3 heart sound intensity to areference heart sound intensity, timing of the S1, S2, S3, or S4 heartsound with respect to a fiducial point such as a P wave, Q wave, or Rwave in an ECG, a respiratory rate, a tidal volume, a RSBI, physicalactivity intensity, or a time duration when the activity intensity iswithin a specified range or above a specified threshold, systolic bloodpressure, diastolic blood pressure, mean arterial pressure, or thetiming metrics of these pressure measurements with respect to a fiducialpoint, among others. A signal metric trend may include multiplemeasurements of the signal metric during a specified period of time. Inan example, the signal metric trend may include a daily trend includingdaily measurement of a signal metric over a specified number of days.

At 630, a cardiac risk indicating a risk of the patient developing afuture worsening cardiac event may be generated from one or more signalmetrics of the physiological signal, such as by using the riskstratifier circuit 230 as shown in FIG. 2 . The signal metrics forassessing cardiac risk may be different from the signal metrics fordetecting the cardiac event. In an example, the signal metrics forcardiac risk assessment may include intensity of a heart sound componentsuch as S3 heart sound measured from a heart sound signal, a respiratoryrate or tidal volume measured from a respiration signal, thoracicimpedance measured from an impedance signal such as using electrodes onone or more implantable leads and implantable device can housing, orphysical activity intensity level measured from an physical activitysignal such as using an ambulatory accelerometer associated with thepatient. Examples of generating the cardiac risk using a plurality ofsignal metrics are discussed below, such as with reference to FIG. 9 .

At 640, primary and secondary detection indications may be generatedsuch as by using the detector circuit 240 as illustrated in FIG. 2 . Theprimary detection indication D1 may be based on temporal change of atleast the first signal metric from a reference level representing asignal metric baseline. In an example, a relative difference between acentral tendency of the first signal metric within a short-term windowand a baseline value determined within a long-term window preceding theshort-term window may be determined, and a worsening cardiac event maybe deemed detected if the relative difference exceeds a specifiedthreshold. The primary detection indication D1 may have discrete orcontinuous values. The secondary detection indication D2 may be based ona temporal change of at least the second signal metric, such as arelative difference between a representative value of the second signalmetric within a short-term time window and baseline value within along-term time window preceding the short-term time window in time. Asdiscussed in the examples with reference to FIGS. 3A-3D, the secondarydetection indication may be generated using a linear, nonlinear, orlogical combination of the relative difference and the risk indication.

At 650, a worsening cardiac event may be detected using the primary andsecondary detection indications. A composite detection indication (CDI)may be generated using a decision tree that includes a logicalcombination of the primary detection indication D1 and the secondarydetection indication D2, such as a Boolean logic “OR” combinationbetween D1 and D2. The decision tree may include a sub-decision treerepresenting a logical combination of the risk indication (R) and thesecond signal metric. In an example, the secondary detection indicationD2 is a Boolean logic “AND” combination between the second signal metricand the risk indication. In various examples, at least one of theprimary or secondary detection indications may include a Boolean-logicor fuzzy-logic combination of two or more signal metrics. The riskindication may similarly include a Boolean-logic or fuzzy-logiccombination of two or more risk indications. Examples of the decisiontree including the primary and secondary detection indications arediscussed below, such as with reference to FIGS. 7A-7B.

At 660, the CDI may be presented to a system user or to a process suchas an alert circuit for producing an alert when the worsening cardiacevent is detected. Additional information that may be displayed includesphysiological signals and the signals metrics, risk indications, orprimary and secondary detection indications, among others. Theinformation may be presented in a table, a chart, a diagram, or anyother types of textual, tabular, or graphical presentation formats, fordisplaying to a system user. The alert may include audio or otherhuman-perceptible media format.

The method 600 may additionally include a step 670 of delivering atherapy to the patient in response to one or more of the primary orsecondary detection indications or the composite detection indication.Examples of the therapy may include electrostimulation therapy deliveredto the heart, a nerve tissue, other target tissues in response to thedetection of the target physiological event, or drug therapy includingdelivering drug to a tissue or organ. In some examples, at 670, theprimary or secondary detection indications or the composite detectionindication may be used to modify an existing therapy, such as adjustinga stimulation parameter or drug dosage.

FIGS. 7A-7B illustrate generally examples of decision trees 750A-B fordetecting the worsening cardiac event. The decision trees 750A-B may beembodiments of the detection of the worsening cardiac event 650 in FIG.6 . The decision trees 750A-B may be implemented as a set of circuits toperform logical combinations of at least the primary and secondarydetection indications. Alternatively, at least a portion of the decisiontree may be implemented in a microprocessor circuit executing a set ofinstructions including logical combinations of at least the primary andsecondary detection indications.

FIG. 7A illustrates an example of a decision tree 750A where the primaryor the secondary detection indication is based on a Boolean-logiccombination of two or more signal metrics. At 751, a heart sound signalmetric of a ratio of S3 to S1 heart sound intensity (S3/S1) may becompared to a threshold T1 to generate a primary detection indicationD1. If S3/S1 exceeds the threshold T1, then the worsening cardiac eventis deemed detected at 754, corresponding to D1=1. If at 751 S3/S1 doesnot exceed the threshold T1, the primary detection indication D1 doesnot indicate a detection of worsening of cardiac event (D1=0), and asecondary detection indication D2 may be generated based on one or moresecond signal metrics at 752A and a risk indication determined at 753A.Steps 752A and 753A form a sub-decision tree for determining thesecondary detection indication D2. The second signal metrics may bechosen from physiological signals that are more sensitive and lessspecific to the worsening cardiac event, such as based on detectionperformance of the signal metrics across a cohort of patients. A moresensitive second signal metric may reduce the false negative detectionof the worsening cardiac event declared by the first signal metric. Inthe example illustrated in FIG. 7A, the second signal metric includes athoracic impedance magnitude (Z) or an rapid-shallow breathing index(RSBI) as a ratio of a respiratory rate to a tidal volume measurement,both of which may be less specific and more sensitive than the S3/S1 indetecting a worsening cardiac event.

A Boolean-logic combination of Z and RSBI such as an “OR” operator maybe used at 752A to determine whether the second signal metric (Z orRSBI) indicates a detection of worsening of heart failure. If either Zor RSBI exceeds the respective threshold T2 or T3, a risk indication maybe generated at 753A to confirm the positive detection declared by thesecond signal metric. The risk indication at 753A includes aBoolean-logic combination of S3 heart sound intensity and respiratoryrate (RR) variability. If both S3 and RR exceed their respectivethresholds T4 and T5, then the detection of the worsening cardiac eventis confirmed at 754, and the process proceeds to step 660 where an alertmay be generated. However, if neither Z nor RSBI exceeds the respectivethreshold T2 or T3 at 752A, or if at least one of S3 or RR does notexceed the respective threshold at 753A, then the secondary detectionindication D2 indicates no detection of the worsening cardiac event at755. The process may proceed to step 610 where the physiological signalsensing and event detection processes continue as illustrated in FIG. 6.

FIG. 7B illustrates an example of a decision tree 750B where the primaryor the secondary detection indication is based on a fuzzy-logiccombination of two or more signal metrics. Similar to the decision tree750A, the decision tree 750B includes a primary detection based on S3/S1at 751 and the positive detection of worsening cardiac event at 754 ifS3/S1 exceeds the threshold T1. If S3/S1 does not exceed T1, a secondarydetection indication D2 may be generated using a sub-decision treeincluding one or more second signal metrics at 752B and a riskindication determined at 753B. In the example as illustrated in FIG. 7B,a fuzzy-logic combination such as “maximum” of Z and RSBI is performedat 752B, and a fuzzy-logic combination such as “minimum” of S3 and RR,is performed at 753B. The operator “maximum” corresponds to the Booleanlogic operator “OR” at 752A, and the operator “minimum” corresponds tothe Boolean logic operator “AND” at 753A. In an example, the two or moresignal metrics in 752B (Z and RSBI) or 753B (S3 and RR) may betransformed into respective fuzzified presentations, and the fuzzy-logiccombination at 752B or 753B may be applied to the fuzzifiedpresentations of the respective signal metrics. If max(Z, RSBI) exceedsthe threshold T6 at 752A, and min(S3, RR) exceeds the threshold T7 at753B, then the detection of the worsening cardiac event is confirmed at754, and the process proceeds to step 660 to generate an alert of thedetected worsening cardiac event. However, if max (Z, RSBI) does notexceed the threshold T6 at 752B, or if min (S3, RR) does not exceed thethreshold T7 at 753B, then the secondary detection indication D2indicates no detection of the worsening cardiac event at 755; and theprocess proceeds to step 610 where the physiological signal sensing andevent detection processes continue as illustrated in FIG. 6 .

FIG. 8 illustrates generally an example of a portion of a method 800 fordetecting a worsening cardiac event based at least on the first andsecond signal metrics. The method 800 may be in addition to or as analternative of the steps 640 and 650 for detecting worsening cardiacevent based on the primary and secondary detection indications. At 810,the first and second signal metric trends, such as those generated at620, may be transformed into a unified scale. In an example, a temporalchange of the first signal metric (such as a relative difference betweena short-term window and a baseline value computed from a long-termwindow) may be transformed into a first sequence of transformed indiceswithin a specified range. A temporal change of the second signal metricmay similarly be transformed into a second sequence of transformedindices within the same specified range, such that the transformed firstand second signal metric trends may be easily compared or combined. Inan example, the transformation of the first and second signal metrictrend may be based on respective codebook that maps quantized magnitudeof respective signal metric into numerical indices within a specifiedrange, where a larger code indicates a higher signal magnitude. In anexample, the transformed indices may be obtained from a transformationof linear or nonlinear combination of more than one signal metrics.

At 820, the second signal metric may be modulated by the cardiac riskindication. In an example, the modulation of the second signal metricmay include a scaled temporal change of the second signal metricweighted by the risk indication. As illustrated in FIG. 3A, the riskindication may take discrete values such as “0” or “1”, such as to gatethe contribution of temporal change to the secondary detectionindication. The risk indication R may alternatively take real numberssuch as between 0 and 1, and weight the contribution of temporal changeto the secondary detection indication. In another example, themodulation of the second signal metric may include a sampled temporalchange of the second signal metric when the risk indication satisfies aspecified condition. In an example as illustrated in FIG. 3B, the secondsignal metric may include a respiratory rate trend, and the riskindication may include a physical activity intensity. The respiratoryrate trend may be sampled during a time period when the physicalactivity intensity exceeds a specified threshold, and the secondarydetection indication may be determined as a statistical measure, such asa central tendency or a variability, of the conditionally sampled RRmeasurements.

At 830, a composite signal trend cY may be generated using thetransformed first signal metric Y1 and the second signal metric Y2modulated by R. The combination may include a linear or nonlinearcombination, such as shown in Equation (4) as previously discussed. Inan example, the composite signal trend cY is a linear combination of Y1and Y2*R. In another example, the composite signal trend cY is a linearcombination of Y1 and conditionally-sampled Y2 upon R satisfying aspecified condition. The composite signal trend cY may then be comparedto a threshold at 840. If cY exceeds the threshold, then the worseningcardiac event is deemed detected, and an alert is generated at 660. IfcY does not exceed the threshold, then no worsening cardiac event isdeemed detected, and the process may proceed to step 610 where thephysiological signal sensing and event detection processes continue asillustrated in FIG. 6 . In an example, an alert can be generated if cYexceeds a first threshold. The alert may sustain until cY falls below asecond threshold indicating a recovery or improvement of thephysiological status.

FIG. 9 illustrates generally an example of a method 930 for cardiac riskassessment. The method 930 may be an embodiment of the step 630 of FIG.6 , and may be implemented in and executed by the risk stratifiercircuit 230 of FIG. 2 or the risk stratifier circuit 400 of FIG. 4 .

The method 930 begins at 931 by generating a primary risk indication forcardiac risk assessment from a first signal metric (X1_(R)) for cardiacrisk assessment. The signal metric X1_(R) may be different from thesignal metrics used for detecting worsening cardiac event. In anexample, the first signal metric X1_(R) may be extracted from a heartsound signal, and include one of a S3 intensity, or a ratio of a S3intensity to a reference heart sound intensity such as one of S1intensity, S2 intensity, or heart sound energy during a specifiedportion of the cardiac cycle. In an example, the primary cardiac riskindication may include a statistical measure, such as a central tendencya variability, of the plurality of the measurements of the signal metricX1_(R).

At 932, a plurality of measurements of a third signal metric 223(X3_(R)) for cardiac risk assessment may be taken. X3_(R) may bedifferent from the signal metric X1_(R) for cardiac risk assessment. At933, the X3_(R) may be compared to a specified condition (such as athreshold) to control a conditional sampling of a second signal metricX2_(R). If X3_(R) satisfies the specified condition, a plurality ofmeasurements of the second signal metric X2_(R) may be sampled at 934.In an example, the second cardiac signal metric X2_(R) may include arespiratory rate and the third cardiac signal metric X3_(R) may includephysical activity intensity or the duration of the physical activityabove a threshold. The respiratory rate measurements may be sampledduring a time period when a high physical activity is indicated, such aswhen the physical activity intensity exceeds a specified threshold.Other examples of the signal metric X2_(R) may include a tidal volume, arapid-shallow breathing index (RSBI) computed as a ratio of therespiratory rate to the tidal volume, or a thoracic impedance magnitudeindicating thoracic fluid accumulation, among others. Other examples ofX3_(R) may include time of day, metabolic state, or heart rate, amongothers.

At 935, a secondary cardiac risk indication may be generated. An exampleof the secondary cardiac risk indication may include a statisticalmeasure, such as a central tendency or a variability, of the sampledrespiratory rate measurements.

At 936, the primary and secondary risk indications R1 and R2 may becombined to generate a composite cardiac risk indication (cR), such asaccording to a fusion model. The fusion model may include one or moresignal metrics and an algorithm for transforming the one or more signalmetrics into a risk indication. Examples of the fusion models mayinclude a linear weighted combination, a nonlinear combination such as adecision tree, a neural network, a fuzzy-logic model, or a multivariateregression model, among others. In an example, a fusion model may beselected according signal quality of the one or more physiologicalsignals from which the cardiac signal metrics X1_(R), X2_(R), or X3_(R)are generated. For example, a first candidate fusion model that employsa physiological signal with a higher signal-to-noise ratio (SNR) may beselected over a second candidate fusion model that employs aphysiological signal with a lower SNR. The composite cardiac riskindication cR may be generated by combining a first statistic of aplurality of measurements of the signal metric X1_(R) and a secondstatistic of a plurality of measurements of the combined metric betweenX2_(R) and X3_(R). Examples of the first and second statistics may eachinclude a first-order statistic such as a central tendency measure or asecond-order statistic such as a variability measure. In an example, theprimary cardiac risk indication R1 includes a central tendency or otherfirst-order statistics of a plurality of measurements of the signalmetric X1_(R), and the secondary cardiac risk indication R2 includes avariability or other second-order statistics of a plurality ofmeasurements of the linearly or nonlinearly combined metric betweenX2_(R) and X3_(R) or conditionally sampled X2_(R). The composite cardiacrisk indication cR may be generated by combining the central tendency ofX1_(R) and the variability of the X2_(R) or the variability of thecombined X2_(R) and X3_(R).

At 937, the cardiac risk indications R1 or R2 may be adjusted accordingto information about the patient clinical indications. The clinicalindications may include patient medical history such as historicalcardiac events, heart failure comorbidities or other concomitant diseasestates, exacerbation of recent chronic disease, a previous medicalprocedure, a clinical lab test result, patient medication intake orother treatment undertaken, patient physical assessment, or patientdemographics such as age, gender, race, or ethnicity. In an example, thecomposite cardiac risk indication cR may be adjusted by the clinician.In an example, at least one of the primary or secondary risk indicationsR1 or R2 may be weighted by time-varying weight factors that decay withtime elapsed from a historical medical event may be applied to. Thetime-varying weight factor may follow a linear, exponential, or othernonlinear decay function of the time elapsed from a historical medicalevent. In another example, at least one of R1 or R2 may be adjustedtemporarily. For example, an elevated risks of R1 or R2 above a baselinerisk score may be applied within a specified timeframe following ahistorical medical event, and resume to the baseline risk score beyondthe specified timeframe. The composite risk indication cR may then beused to generate the second detection indication at 640.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which thedisclosure may be practiced. These embodiments are also referred toherein as “examples.” Such examples may include elements in addition tothose shown or described. However, the present inventors alsocontemplate examples in which only those elements shown or described areprovided. Moreover, the present inventors also contemplate examplesusing any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof), or with respect toother examples (or one or more aspects thereof) shown or describedherein.

In the event of inconsistent usages between this document and anydocuments so incorporated by reference, the usage in this documentcontrols.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, device,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

Method examples described herein may be machine or computer-implementedat least in part. Some examples may include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods may include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code may include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code may be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to complywith 37 C.F.R. § 1.72(b), to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims. Also, in the above Detailed Description,various features may be grouped together to streamline the disclosure.This should not be interpreted as intending that an unclaimed disclosedfeature is essential to any claim. Rather, inventive subject matter maylie in less than all features of a particular disclosed embodiment.Thus, the following claims are hereby incorporated into the DetailedDescription as examples or embodiments, with each claim standing on itsown as a separate embodiment, and it is contemplated that suchembodiments may be combined with each other in various combinations orpermutations. The scope of the disclosure should be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A system for predicting a disease status of apatient, the system comprising: a detector circuit configured to:receive physiological information; determine a risk indicationassociated with the disease status using the received physiologicalinformation; generate a first detection indication using a firstcombination of signal metrics associated with the received physiologicalinformation and respective weight factors; generate a second detectionindication using a second combination of signal metrics associated withthe received physiological information and respective weight factors,wherein the first and second combinations of signal metrics andrespective weight factors are different from each other and based atleast in part on the determined risk indication; and apply aninformation fusion model to the first detection indication, the seconddetection indication, and the risk indication to generate a compositeprediction of the disease status; and an output circuit configured toprovide the composite prediction of the disease status to a user or apatient management process of the system.
 2. The system of claim 1,wherein the information fusion model includes a neural network model. 3.The system of claim 1, wherein the information fusion model includes adecision tree model.
 4. The system of claim 1, wherein the informationfusion model includes a fuzzy-logic model.
 5. The system of claim 1,wherein the information fusion model includes a multivariate regressionmodel.
 6. The system of claim 1, comprising a risk stratifier circuitconfigured to: determine two or more risk factors from respectivedifferent two or more patient physiological or medical informationsources; and determine the risk indication by applying the informationfusion model to the two or more risk factors.
 7. The system of claim 1,wherein to generate the first detection indication includes to determinea first detection score using a trend of a first signal metric overtime, and wherein to generate the second detection indication includesto determine a second detection score using a trend of a second signalmetric over time, the first signal metric selected from the signalmetrics in the first combination, the second signal metric selected fromthe signal metrics in the second combination.
 8. The system of claim 7,the detector circuit is configured to: apply the information fusionmodel to generate a composite score using a weighted combination of thefirst detection score and the second detection score each scaled byrespective weight factors, the respective weight factors determinedbased on the risk indication; and generate the composite prediction ofthe disease status based on the composite score.
 9. The system of claim8, wherein to apply the information fusion model includes to modulatethe second detection score by the risk indication, and to generate thecomposite score using a combination of the first detection score and themodulated second detection score.
 10. The system of claim 8, wherein thedetector circuit is configured to generate the composite prediction ofthe disease status based on a comparison of the composite score to aprediction threshold or a score range.
 11. The system of claim 1,wherein the disease status includes a worsening heart failure, and thesignal metrics in the first combination and the signal metrics in thesecond combination each include at least one of: a heart rate metric; aheart rate variability metric; an arrhythmia metric; anelectrocardiogram or electrogram metric; an impedance metric; a physicalactivity metric; a posture metric; a body temperature or bloodtemperature metric; a heart sound metric; a respiration metric; or ablood pressure metric.
 12. The system of claim 1, wherein the outputcircuit is configured to display, on a user interface, the signalmetrics in the first combination and the signal metrics in the secondcombination, and the first and second detection indications representingrespective contributions to the determined composite prediction of thedisease status.
 13. A method of predicting a disease status of apatient, the method comprising: receiving physiological information ofthe patient; determining a risk indication associated with the diseasestatus using the received physiological information; generating, via adetector circuit, a first detection indication using a first combinationof signal metrics associated with the received physiological informationand respective weight factors; generating a second detection indicationusing a second combination of signal metrics associated with thereceived physiological information and respective weight factors,wherein the first and second combinations of signal metrics andrespective weight factors are different from each other and based atleast in part on the determined risk indication; generating, via thedetector circuit, a composite prediction of the disease status using aninformation fusion model that takes as input the first detectionindication, the second detection indication, and the risk indication;and providing, via an output circuit, the composite prediction of thedisease status to a user or a patient management process.
 14. The methodof claim 13, wherein the information fusion model including a neuralnetwork model.
 15. The method of claim 13, wherein the informationfusion model includes a decision tree model.
 16. The method of claim 13,wherein the information fusion model includes a fuzzy-logic model. 17.The method of claim 13, wherein the information fusion model includes amultivariate regression model.
 18. The method of claim 13, comprisingdetermining two or more risk factors from respective different two ormore patient physiological or medical information sources, whereindetermining the risk indication includes applying the information fusionmodel to the two or more risk factors.
 19. The method of claim 18,wherein the first detection indication includes a first detection scorerepresenting a trend of a first signal metric over time, and the seconddetection indication includes a second detection score representing atrend of a second signal metric over time, the first signal metricselected from the signal metrics in the first combination, the secondsignal metric selected from the signal metrics in the secondcombination, wherein generating the composite prediction of the diseasestatus includes: generating a composite score using a weightedcombination of the first detection score and the second detection scoreeach scaled by respective weight factors, the respective weight factorsdetermined based on the risk indication; and generating the compositeprediction of the disease status based on a comparison of the compositescore to a prediction threshold or a score range.
 20. The method ofclaim 13, comprising displaying, on a user interface, the signal metricsin the first combination and the signal metrics in the secondcombination, and the first and second detection indications representingrespective contributions to the determined composite prediction of thedisease status.